Imaging devices, such as depth sensing systems, can be used to capture images of real-world environments and determine depth for the images using various techniques. For example, some depth sensing systems (i.e., Time-of-Flight (ToF) camera systems) project light onto a real world environment and resolve depth based on the known speed of light and the round trip time-of-flight of light signals in the projected light pattern for each point in an image. However, computing devices including these depth sensing systems suffer ambiguities for depth points in captured images when the number of captured photons in a measured light signal is insufficient to determine depth. For example, an image may include portions which represent objects in the environment whose material properties (e.g., reflectivity) are such that very few photons are reflected (i.e., “dark” portions), or they may represent areas of the environment in which no objects reside to reflect light within a maximum sensing range of the depth sensing system (i.e., “far” portions). When constructing depth maps, these depth sensing systems are unable to provide valid depth data for these dark portions and far portions and may simply mark such portions as invalid in the resulting depth map. However, in some applications of these depth maps, such as surface reconstruction of an environment, this solution may result in visual obscurities or inaccuracies as the default logic for representing dark portions in an image is different than the default logic for representing far portions in an image.